Overview

Dataset statistics

Number of variables21
Number of observations89740
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.5 MiB
Average record size in memory531.9 B

Variable types

Numeric13
Categorical7
Boolean1

Alerts

track_id has a high cardinality: 89740 distinct valuesHigh cardinality
artists has a high cardinality: 31437 distinct valuesHigh cardinality
album_name has a high cardinality: 46589 distinct valuesHigh cardinality
track_name has a high cardinality: 73608 distinct valuesHigh cardinality
track_genre has a high cardinality: 113 distinct valuesHigh cardinality
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
explicit is highly imbalanced (57.8%)Imbalance
time_signature is highly imbalanced (72.5%)Imbalance
track_id is uniformly distributedUniform
track_name is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
track_id has unique valuesUnique
popularity has 9447 (10.5%) zerosZeros
key has 10352 (11.5%) zerosZeros
instrumentalness has 29924 (33.3%) zerosZeros

Reproduction

Analysis started2023-04-14 17:37:31.735983
Analysis finished2023-04-14 17:37:54.634096
Duration22.9 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct89740
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53479.006
Minimum0
Maximum113999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:54.682270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5084.95
Q123766.75
median50680.5
Q380618.5
95-th percentile108333.05
Maximum113999
Range113999
Interquartile range (IQR)56851.75

Descriptive statistics

Standard deviation33410.142
Coefficient of variation (CV)0.62473379
Kurtosis-1.1860874
Mean53479.006
Median Absolute Deviation (MAD)28313.5
Skewness0.16700907
Sum4.799206 × 109
Variance1.1162376 × 109
MonotonicityStrictly increasing
2023-04-14T13:37:54.771313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
70915 1
 
< 0.1%
70926 1
 
< 0.1%
70925 1
 
< 0.1%
70924 1
 
< 0.1%
70923 1
 
< 0.1%
70922 1
 
< 0.1%
70921 1
 
< 0.1%
70919 1
 
< 0.1%
70916 1
 
< 0.1%
Other values (89730) 89730
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
113999 1
< 0.1%
113998 1
< 0.1%
113997 1
< 0.1%
113996 1
< 0.1%
113995 1
< 0.1%
113994 1
< 0.1%
113993 1
< 0.1%
113992 1
< 0.1%
113991 1
< 0.1%
113990 1
< 0.1%

track_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct89740
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
5SuOikwiRyPMVoIQDJUgSV
 
1
5POo0zRDYRZhU0SMs49Rfo
 
1
61suxeOgSBDfwySo1O3BQ4
 
1
1o1pZgOTPl2eEdRAND2UsQ
 
1
7x2It3MYN1QdytzgoLYGr5
 
1
Other values (89735)
89735 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1974280
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89740 ?
Unique (%)100.0%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K

Common Values

ValueCountFrequency (%)
5SuOikwiRyPMVoIQDJUgSV 1
 
< 0.1%
5POo0zRDYRZhU0SMs49Rfo 1
 
< 0.1%
61suxeOgSBDfwySo1O3BQ4 1
 
< 0.1%
1o1pZgOTPl2eEdRAND2UsQ 1
 
< 0.1%
7x2It3MYN1QdytzgoLYGr5 1
 
< 0.1%
33xG0TlibLH8kO4At9cjkA 1
 
< 0.1%
6LB5GOIP5MMZnMUuEt6CxA 1
 
< 0.1%
1Rxn57Wwv6hQUu60MIiMMJ 1
 
< 0.1%
4hcNpo1rtbzyNswsM5byTf 1
 
< 0.1%
7gwZ37Vng40BDAKuiBMsAZ 1
 
< 0.1%
Other values (89730) 89730
> 99.9%

Length

2023-04-14T13:37:54.856079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5suoikwirypmvoiqdjugsv 1
 
< 0.1%
0x9mxhr1rtkehdjp95f2oo 1
 
< 0.1%
01mvol9ktvtnffibu9i7dc 1
 
< 0.1%
6vc5wammxdkiam7wuoeb7n 1
 
< 0.1%
1ezreoxmmh3g43axt1y7pa 1
 
< 0.1%
0iktbucnagrvd03awnz3q8 1
 
< 0.1%
7k9gujylp2azqokyedwew2 1
 
< 0.1%
210jcw2lbyd4yis8giz9ip 1
 
< 0.1%
4mzp5mhkrvgxdhdgdah7ej 1
 
< 0.1%
4ptdjbjl35d7gqfentebwp 1
 
< 0.1%
Other values (89730) 89730
> 99.9%

Most occurring characters

ValueCountFrequency (%)
3 42177
 
2.1%
6 42169
 
2.1%
5 42049
 
2.1%
4 41963
 
2.1%
2 41947
 
2.1%
1 41908
 
2.1%
0 41817
 
2.1%
7 39664
 
2.0%
K 30792
 
1.6%
k 30733
 
1.6%
Other values (52) 1579061
80.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 789876
40.0%
Lowercase Letter 789774
40.0%
Decimal Number 394630
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 30792
 
3.9%
A 30715
 
3.9%
D 30685
 
3.9%
G 30655
 
3.9%
X 30541
 
3.9%
L 30526
 
3.9%
E 30522
 
3.9%
I 30516
 
3.9%
B 30497
 
3.9%
M 30468
 
3.9%
Other values (16) 483959
61.3%
Lowercase Letter
ValueCountFrequency (%)
k 30733
 
3.9%
y 30642
 
3.9%
h 30637
 
3.9%
f 30630
 
3.9%
l 30551
 
3.9%
m 30505
 
3.9%
p 30491
 
3.9%
c 30482
 
3.9%
b 30462
 
3.9%
w 30458
 
3.9%
Other values (16) 484183
61.3%
Decimal Number
ValueCountFrequency (%)
3 42177
10.7%
6 42169
10.7%
5 42049
10.7%
4 41963
10.6%
2 41947
10.6%
1 41908
10.6%
0 41817
10.6%
7 39664
10.1%
8 30567
7.7%
9 30369
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1579650
80.0%
Common 394630
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 30792
 
1.9%
k 30733
 
1.9%
A 30715
 
1.9%
D 30685
 
1.9%
G 30655
 
1.9%
y 30642
 
1.9%
h 30637
 
1.9%
f 30630
 
1.9%
l 30551
 
1.9%
X 30541
 
1.9%
Other values (42) 1273069
80.6%
Common
ValueCountFrequency (%)
3 42177
10.7%
6 42169
10.7%
5 42049
10.7%
4 41963
10.6%
2 41947
10.6%
1 41908
10.6%
0 41817
10.6%
7 39664
10.1%
8 30567
7.7%
9 30369
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1974280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 42177
 
2.1%
6 42169
 
2.1%
5 42049
 
2.1%
4 41963
 
2.1%
2 41947
 
2.1%
1 41908
 
2.1%
0 41817
 
2.1%
7 39664
 
2.0%
K 30792
 
1.6%
k 30733
 
1.6%
Other values (52) 1579061
80.0%

artists
Categorical

Distinct31437
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
George Jones
 
260
my little airport
 
171
The Beatles
 
149
BTS
 
143
Håkan Hellström
 
141
Other values (31432)
88876 

Length

Max length513
Median length322
Mean length16.377056
Min length2

Characters and Unicode

Total characters1469677
Distinct characters712
Distinct categories18 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20150 ?
Unique (%)22.5%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet

Common Values

ValueCountFrequency (%)
George Jones 260
 
0.3%
my little airport 171
 
0.2%
The Beatles 149
 
0.2%
BTS 143
 
0.2%
Håkan Hellström 141
 
0.2%
Glee Cast 139
 
0.2%
Hank Williams 136
 
0.2%
Linkin Park 133
 
0.1%
Scooter 130
 
0.1%
OneRepublic 124
 
0.1%
Other values (31427) 88214
98.3%

Length

2023-04-14T13:37:54.946583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 4979
 
2.4%
2441
 
1.2%
de 928
 
0.5%
los 865
 
0.4%
of 827
 
0.4%
dj 599
 
0.3%
george 527
 
0.3%
jones 424
 
0.2%
la 406
 
0.2%
for 396
 
0.2%
Other values (42276) 190938
93.9%

Most occurring characters

ValueCountFrequency (%)
a 129670
 
8.8%
e 117023
 
8.0%
113598
 
7.7%
i 89038
 
6.1%
n 83644
 
5.7%
o 83448
 
5.7%
r 81021
 
5.5%
l 59431
 
4.0%
s 55463
 
3.8%
t 50897
 
3.5%
Other values (702) 606444
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1041824
70.9%
Uppercase Letter 263603
 
17.9%
Space Separator 113598
 
7.7%
Other Punctuation 41593
 
2.8%
Decimal Number 4569
 
0.3%
Other Letter 1946
 
0.1%
Dash Punctuation 1673
 
0.1%
Currency Symbol 179
 
< 0.1%
Close Punctuation 171
 
< 0.1%
Open Punctuation 169
 
< 0.1%
Other values (8) 352
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
63
 
3.2%
59
 
3.0%
55
 
2.8%
49
 
2.5%
43
 
2.2%
42
 
2.2%
41
 
2.1%
41
 
2.1%
31
 
1.6%
25
 
1.3%
Other values (453) 1497
76.9%
Lowercase Letter
ValueCountFrequency (%)
a 129670
12.4%
e 117023
11.2%
i 89038
 
8.5%
n 83644
 
8.0%
o 83448
 
8.0%
r 81021
 
7.8%
l 59431
 
5.7%
s 55463
 
5.3%
t 50897
 
4.9%
h 39724
 
3.8%
Other values (102) 252465
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 22879
 
8.7%
M 18968
 
7.2%
A 18477
 
7.0%
B 17099
 
6.5%
C 16293
 
6.2%
T 16054
 
6.1%
D 14420
 
5.5%
R 13434
 
5.1%
L 13081
 
5.0%
P 12379
 
4.7%
Other values (66) 100519
38.1%
Other Punctuation
ValueCountFrequency (%)
; 33684
81.0%
. 3164
 
7.6%
& 2334
 
5.6%
' 1171
 
2.8%
" 440
 
1.1%
! 270
 
0.6%
, 238
 
0.6%
/ 121
 
0.3%
: 114
 
0.3%
? 29
 
0.1%
Other values (9) 28
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 693
15.2%
2 666
14.6%
4 620
13.6%
3 533
11.7%
0 490
10.7%
6 361
7.9%
8 355
7.8%
7 294
6.4%
9 287
6.3%
5 270
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 144
84.2%
] 17
 
9.9%
5
 
2.9%
3
 
1.8%
} 2
 
1.2%
Math Symbol
ValueCountFrequency (%)
+ 72
58.1%
= 33
26.6%
17
 
13.7%
| 1
 
0.8%
1
 
0.8%
Other Symbol
ValueCountFrequency (%)
6
37.5%
5
31.2%
2
 
12.5%
® 2
 
12.5%
1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 144
85.2%
[ 17
 
10.1%
5
 
3.0%
3
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 1611
96.3%
62
 
3.7%
Modifier Letter
ValueCountFrequency (%)
115
98.3%
2
 
1.7%
Final Punctuation
ValueCountFrequency (%)
49
98.0%
1
 
2.0%
Initial Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
113598
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 179
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 11
100.0%
Other Number
ValueCountFrequency (%)
² 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1299629
88.4%
Common 162309
 
11.0%
Cyrillic 5788
 
0.4%
Han 1246
 
0.1%
Katakana 615
 
< 0.1%
Hiragana 87
 
< 0.1%
Greek 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
31
 
2.5%
25
 
2.0%
23
 
1.8%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
18
 
1.4%
18
 
1.4%
16
 
1.3%
Other values (380) 1042
83.6%
Latin
ValueCountFrequency (%)
a 129670
 
10.0%
e 117023
 
9.0%
i 89038
 
6.9%
n 83644
 
6.4%
o 83448
 
6.4%
r 81021
 
6.2%
l 59431
 
4.6%
s 55463
 
4.3%
t 50897
 
3.9%
h 39724
 
3.1%
Other values (120) 510270
39.3%
Common
ValueCountFrequency (%)
113598
70.0%
; 33684
 
20.8%
. 3164
 
1.9%
& 2334
 
1.4%
- 1611
 
1.0%
' 1171
 
0.7%
1 693
 
0.4%
2 666
 
0.4%
4 620
 
0.4%
3 533
 
0.3%
Other values (51) 4235
 
2.6%
Cyrillic
ValueCountFrequency (%)
а 723
 
12.5%
о 436
 
7.5%
р 412
 
7.1%
и 388
 
6.7%
е 352
 
6.1%
н 338
 
5.8%
к 250
 
4.3%
в 246
 
4.3%
л 232
 
4.0%
с 200
 
3.5%
Other values (45) 2211
38.2%
Katakana
ValueCountFrequency (%)
63
 
10.2%
59
 
9.6%
55
 
8.9%
49
 
8.0%
43
 
7.0%
42
 
6.8%
41
 
6.7%
41
 
6.7%
19
 
3.1%
17
 
2.8%
Other values (35) 186
30.2%
Hiragana
ValueCountFrequency (%)
8
 
9.2%
8
 
9.2%
7
 
8.0%
6
 
6.9%
6
 
6.9%
6
 
6.9%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
Other values (19) 29
33.3%
Greek
ValueCountFrequency (%)
α 2
66.7%
μ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1454103
98.9%
None 7571
 
0.5%
Cyrillic 5788
 
0.4%
CJK 1244
 
0.1%
Katakana 731
 
< 0.1%
Punctuation 118
 
< 0.1%
Hiragana 87
 
< 0.1%
Math Operators 18
 
< 0.1%
Misc Symbols 6
 
< 0.1%
Letterlike Symbols 5
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 129670
 
8.9%
e 117023
 
8.0%
113598
 
7.8%
i 89038
 
6.1%
n 83644
 
5.8%
o 83448
 
5.7%
r 81021
 
5.6%
l 59431
 
4.1%
s 55463
 
3.8%
t 50897
 
3.5%
Other values (78) 590870
40.6%
None
ValueCountFrequency (%)
é 1257
16.6%
ã 643
 
8.5%
á 577
 
7.6%
ö 575
 
7.6%
ó 502
 
6.6%
í 467
 
6.2%
ä 401
 
5.3%
ü 361
 
4.8%
ç 307
 
4.1%
ı 215
 
2.8%
Other values (80) 2266
29.9%
Cyrillic
ValueCountFrequency (%)
а 723
 
12.5%
о 436
 
7.5%
р 412
 
7.1%
и 388
 
6.7%
е 352
 
6.1%
н 338
 
5.8%
к 250
 
4.3%
в 246
 
4.3%
л 232
 
4.0%
с 200
 
3.5%
Other values (45) 2211
38.2%
Katakana
ValueCountFrequency (%)
115
15.7%
63
 
8.6%
59
 
8.1%
55
 
7.5%
49
 
6.7%
43
 
5.9%
42
 
5.7%
41
 
5.6%
41
 
5.6%
19
 
2.6%
Other values (37) 204
27.9%
Punctuation
ValueCountFrequency (%)
62
52.5%
49
41.5%
4
 
3.4%
1
 
0.8%
1
 
0.8%
1
 
0.8%
CJK
ValueCountFrequency (%)
31
 
2.5%
25
 
2.0%
23
 
1.8%
19
 
1.5%
18
 
1.4%
18
 
1.4%
18
 
1.4%
18
 
1.4%
18
 
1.4%
16
 
1.3%
Other values (379) 1040
83.6%
Math Operators
ValueCountFrequency (%)
17
94.4%
1
 
5.6%
Hiragana
ValueCountFrequency (%)
8
 
9.2%
8
 
9.2%
7
 
8.0%
6
 
6.9%
6
 
6.9%
6
 
6.9%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
Other values (19) 29
33.3%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
66.7%
1
33.3%
Dingbats
ValueCountFrequency (%)
2
66.7%
1
33.3%

album_name
Categorical

Distinct46589
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
The Complete Hank Williams
 
110
Greatest Hits
 
77
Mozart: A Night of Classics
 
75
Alternative Christmas 2022
 
73
Mozart - All Day Classics
 
68
Other values (46584)
89337 

Length

Max length243
Median length145
Mean length20.23908
Min length1

Characters and Unicode

Total characters1816255
Distinct characters2084
Distinct categories22 ?
Distinct scripts13 ?
Distinct blocks22 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33270 ?
Unique (%)37.1%

Sample

1st rowComedy
2nd rowGhost (Acoustic)
3rd rowTo Begin Again
4th rowCrazy Rich Asians (Original Motion Picture Soundtrack)
5th rowHold On

Common Values

ValueCountFrequency (%)
The Complete Hank Williams 110
 
0.1%
Greatest Hits 77
 
0.1%
Mozart: A Night of Classics 75
 
0.1%
Alternative Christmas 2022 73
 
0.1%
Mozart - All Day Classics 68
 
0.1%
Hans Zimmer: Epic Scores 68
 
0.1%
Classical Christmas 61
 
0.1%
Best Of Karneval 58
 
0.1%
Mozart - Inspiring Classics 58
 
0.1%
Karneval 2022 100% 57
 
0.1%
Other values (46579) 89035
99.2%

Length

2023-04-14T13:37:55.050426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 9990
 
3.2%
7278
 
2.3%
of 4363
 
1.4%
vivo 2574
 
0.8%
a 2497
 
0.8%
original 2474
 
0.8%
vol 2417
 
0.8%
de 2361
 
0.8%
ao 2329
 
0.8%
soundtrack 1998
 
0.6%
Other values (35981) 271487
87.6%

Most occurring characters

ValueCountFrequency (%)
220028
 
12.1%
e 145205
 
8.0%
a 112750
 
6.2%
o 109283
 
6.0%
i 102012
 
5.6%
n 85476
 
4.7%
r 83127
 
4.6%
t 75365
 
4.1%
s 75296
 
4.1%
l 62264
 
3.4%
Other values (2074) 745449
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1188084
65.4%
Uppercase Letter 289675
 
15.9%
Space Separator 220028
 
12.1%
Decimal Number 35416
 
1.9%
Other Punctuation 26214
 
1.4%
Other Letter 19850
 
1.1%
Close Punctuation 14800
 
0.8%
Open Punctuation 14796
 
0.8%
Dash Punctuation 5756
 
0.3%
Math Symbol 703
 
< 0.1%
Other values (12) 933
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
342
 
1.7%
323
 
1.6%
278
 
1.4%
247
 
1.2%
235
 
1.2%
211
 
1.1%
202
 
1.0%
177
 
0.9%
174
 
0.9%
160
 
0.8%
Other values (1728) 17501
88.2%
Lowercase Letter
ValueCountFrequency (%)
e 145205
12.2%
a 112750
 
9.5%
o 109283
 
9.2%
i 102012
 
8.6%
n 85476
 
7.2%
r 83127
 
7.0%
t 75365
 
6.3%
s 75296
 
6.3%
l 62264
 
5.2%
u 41837
 
3.5%
Other values (128) 295469
24.9%
Uppercase Letter
ValueCountFrequency (%)
S 24729
 
8.5%
T 22303
 
7.7%
A 20565
 
7.1%
M 18192
 
6.3%
C 17686
 
6.1%
P 14816
 
5.1%
R 14786
 
5.1%
B 14531
 
5.0%
E 14403
 
5.0%
D 14278
 
4.9%
Other values (91) 113386
39.1%
Other Punctuation
ValueCountFrequency (%)
. 7340
28.0%
, 4167
15.9%
' 3858
14.7%
: 3675
14.0%
& 1965
 
7.5%
/ 1537
 
5.9%
" 1322
 
5.0%
! 1055
 
4.0%
? 408
 
1.6%
207
 
0.8%
Other values (13) 680
 
2.6%
Nonspacing Mark
ValueCountFrequency (%)
57
53.3%
́ 11
 
10.3%
̆ 11
 
10.3%
8
 
7.5%
̈ 6
 
5.6%
4
 
3.7%
3
 
2.8%
̀ 2
 
1.9%
1
 
0.9%
1
 
0.9%
Other values (3) 3
 
2.8%
Decimal Number
ValueCountFrequency (%)
2 12173
34.4%
0 8464
23.9%
1 5611
15.8%
9 1977
 
5.6%
3 1826
 
5.2%
5 1477
 
4.2%
4 1182
 
3.3%
6 916
 
2.6%
7 907
 
2.6%
8 883
 
2.5%
Math Symbol
ValueCountFrequency (%)
+ 254
36.1%
~ 242
34.4%
> 64
 
9.1%
< 61
 
8.7%
| 50
 
7.1%
= 15
 
2.1%
7
 
1.0%
× 7
 
1.0%
÷ 2
 
0.3%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 13986
94.5%
] 659
 
4.5%
48
 
0.3%
48
 
0.3%
43
 
0.3%
10
 
0.1%
} 5
 
< 0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 13984
94.5%
[ 657
 
4.4%
48
 
0.3%
48
 
0.3%
43
 
0.3%
10
 
0.1%
{ 5
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 20
34.5%
° 17
29.3%
14
24.1%
2
 
3.4%
2
 
3.4%
1
 
1.7%
1
 
1.7%
1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 5547
96.4%
143
 
2.5%
56
 
1.0%
6
 
0.1%
3
 
0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 20
87.0%
` 2
 
8.7%
˚ 1
 
4.3%
Letter Number
ValueCountFrequency (%)
15
75.0%
4
 
20.0%
1
 
5.0%
Spacing Mark
ValueCountFrequency (%)
5
55.6%
3
33.3%
1
 
11.1%
Modifier Letter
ValueCountFrequency (%)
339
95.0%
18
 
5.0%
Final Punctuation
ValueCountFrequency (%)
159
78.7%
43
 
21.3%
Initial Punctuation
ValueCountFrequency (%)
51
89.5%
6
 
10.5%
Format
ValueCountFrequency (%)
14
87.5%
2
 
12.5%
Space Separator
ValueCountFrequency (%)
220028
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 55
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 28
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1461370
80.5%
Common 318492
 
17.5%
Cyrillic 16353
 
0.9%
Han 12254
 
0.7%
Katakana 4408
 
0.2%
Hiragana 3087
 
0.2%
Inherited 98
 
< 0.1%
Greek 67
 
< 0.1%
Arabic 44
 
< 0.1%
Malayalam 38
 
< 0.1%
Other values (3) 44
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
278
 
2.3%
211
 
1.7%
174
 
1.4%
125
 
1.0%
125
 
1.0%
122
 
1.0%
118
 
1.0%
111
 
0.9%
106
 
0.9%
98
 
0.8%
Other values (1537) 10786
88.0%
Latin
ValueCountFrequency (%)
e 145205
 
9.9%
a 112750
 
7.7%
o 109283
 
7.5%
i 102012
 
7.0%
n 85476
 
5.8%
r 83127
 
5.7%
t 75365
 
5.2%
s 75296
 
5.2%
l 62264
 
4.3%
u 41837
 
2.9%
Other values (144) 568755
38.9%
Common
ValueCountFrequency (%)
220028
69.1%
) 13986
 
4.4%
( 13984
 
4.4%
2 12173
 
3.8%
0 8464
 
2.7%
. 7340
 
2.3%
1 5611
 
1.8%
- 5547
 
1.7%
, 4167
 
1.3%
' 3858
 
1.2%
Other values (77) 23334
 
7.3%
Katakana
ValueCountFrequency (%)
342
 
7.8%
247
 
5.6%
235
 
5.3%
177
 
4.0%
160
 
3.6%
155
 
3.5%
149
 
3.4%
142
 
3.2%
138
 
3.1%
136
 
3.1%
Other values (68) 2527
57.3%
Hiragana
ValueCountFrequency (%)
323
 
10.5%
202
 
6.5%
157
 
5.1%
144
 
4.7%
129
 
4.2%
123
 
4.0%
105
 
3.4%
100
 
3.2%
95
 
3.1%
92
 
3.0%
Other values (60) 1617
52.4%
Cyrillic
ValueCountFrequency (%)
е 1446
 
8.8%
а 1319
 
8.1%
о 1250
 
7.6%
н 1116
 
6.8%
с 1105
 
6.8%
и 1101
 
6.7%
р 764
 
4.7%
т 761
 
4.7%
л 614
 
3.8%
к 613
 
3.7%
Other values (53) 6264
38.3%
Greek
ValueCountFrequency (%)
φ 14
20.9%
α 6
 
9.0%
Ξ 5
 
7.5%
ς 4
 
6.0%
Ψ 3
 
4.5%
μ 3
 
4.5%
τ 2
 
3.0%
ε 2
 
3.0%
η 2
 
3.0%
υ 2
 
3.0%
Other values (16) 24
35.8%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Hangul
ValueCountFrequency (%)
6
16.7%
4
11.1%
3
8.3%
3
8.3%
3
8.3%
3
8.3%
3
8.3%
2
 
5.6%
2
 
5.6%
1
 
2.8%
Other values (6) 6
16.7%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
ت 4
9.1%
ه 4
9.1%
و 4
9.1%
ر 4
9.1%
چ 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Inherited
ValueCountFrequency (%)
57
58.2%
́ 11
 
11.2%
̆ 11
 
11.2%
8
 
8.2%
̈ 6
 
6.1%
̀ 2
 
2.0%
̃ 1
 
1.0%
1
 
1.0%
̊ 1
 
1.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1767325
97.3%
Cyrillic 16353
 
0.9%
CJK 12235
 
0.7%
None 11604
 
0.6%
Katakana 4954
 
0.3%
Hiragana 3152
 
0.2%
Punctuation 392
 
< 0.1%
Arabic 44
 
< 0.1%
Malayalam 38
 
< 0.1%
Hangul 36
 
< 0.1%
Other values (12) 122
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220028
 
12.4%
e 145205
 
8.2%
a 112750
 
6.4%
o 109283
 
6.2%
i 102012
 
5.8%
n 85476
 
4.8%
r 83127
 
4.7%
t 75365
 
4.3%
s 75296
 
4.3%
l 62264
 
3.5%
Other values (83) 696519
39.4%
Cyrillic
ValueCountFrequency (%)
е 1446
 
8.8%
а 1319
 
8.1%
о 1250
 
7.6%
н 1116
 
6.8%
с 1105
 
6.8%
и 1101
 
6.7%
р 764
 
4.7%
т 761
 
4.7%
л 614
 
3.8%
к 613
 
3.7%
Other values (53) 6264
38.3%
None
ValueCountFrequency (%)
ó 936
 
8.1%
ã 925
 
8.0%
é 898
 
7.7%
á 787
 
6.8%
ç 685
 
5.9%
í 602
 
5.2%
ü 589
 
5.1%
ú 575
 
5.0%
ä 561
 
4.8%
ı 532
 
4.6%
Other values (132) 4514
38.9%
Katakana
ValueCountFrequency (%)
342
 
6.9%
339
 
6.8%
247
 
5.0%
235
 
4.7%
207
 
4.2%
177
 
3.6%
160
 
3.2%
155
 
3.1%
149
 
3.0%
142
 
2.9%
Other values (70) 2801
56.5%
Hiragana
ValueCountFrequency (%)
323
 
10.2%
202
 
6.4%
157
 
5.0%
144
 
4.6%
129
 
4.1%
123
 
3.9%
105
 
3.3%
100
 
3.2%
95
 
3.0%
92
 
2.9%
Other values (62) 1682
53.4%
CJK
ValueCountFrequency (%)
278
 
2.3%
211
 
1.7%
174
 
1.4%
125
 
1.0%
125
 
1.0%
122
 
1.0%
118
 
1.0%
111
 
0.9%
106
 
0.9%
98
 
0.8%
Other values (1535) 10767
88.0%
Punctuation
ValueCountFrequency (%)
159
40.6%
56
 
14.3%
51
 
13.0%
43
 
11.0%
42
 
10.7%
14
 
3.6%
6
 
1.5%
6
 
1.5%
6
 
1.5%
3
 
0.8%
Other values (3) 6
 
1.5%
IPA Ext
ValueCountFrequency (%)
ə 20
100.0%
Number Forms
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
Misc Symbols
ValueCountFrequency (%)
14
77.8%
2
 
11.1%
2
 
11.1%
Diacriticals
ValueCountFrequency (%)
́ 11
34.4%
̆ 11
34.4%
̈ 6
18.8%
̀ 2
 
6.2%
̃ 1
 
3.1%
̊ 1
 
3.1%
Arabic
ValueCountFrequency (%)
ا 8
18.2%
ی 6
13.6%
ت 4
9.1%
ه 4
9.1%
و 4
9.1%
ر 4
9.1%
چ 4
9.1%
ک 4
9.1%
ل 4
9.1%
م 2
 
4.5%
Math Operators
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Hangul
ValueCountFrequency (%)
6
16.7%
4
11.1%
3
8.3%
3
8.3%
3
8.3%
3
8.3%
3
8.3%
2
 
5.6%
2
 
5.6%
1
 
2.8%
Other values (6) 6
16.7%
Malayalam
ValueCountFrequency (%)
5
13.2%
4
10.5%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (8) 8
21.1%
Latin Ext Additional
ValueCountFrequency (%)
3
25.0%
3
25.0%
2
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
Devanagari
ValueCountFrequency (%)
3
100.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Dingbats
ValueCountFrequency (%)
1
50.0%
1
50.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%

track_name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct73608
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
Rockin' Around The Christmas Tree
 
48
Frosty The Snowman
 
45
Little Saint Nick - 1991 Remix
 
41
Run Rudolph Run
 
40
Santa Claus Is Coming To Town
 
38
Other values (73603)
89528 

Length

Max length511
Median length146
Mean length18.158257
Min length1

Characters and Unicode

Total characters1629522
Distinct characters2417
Distinct categories23 ?
Distinct scripts13 ?
Distinct blocks25 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65617 ?
Unique (%)73.1%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On

Common Values

ValueCountFrequency (%)
Rockin' Around The Christmas Tree 48
 
0.1%
Frosty The Snowman 45
 
0.1%
Little Saint Nick - 1991 Remix 41
 
< 0.1%
Run Rudolph Run 40
 
< 0.1%
Santa Claus Is Coming To Town 38
 
< 0.1%
Let It Snow! Let It Snow! Let It Snow! 38
 
< 0.1%
Winter Wonderland 37
 
< 0.1%
Have Yourself A Merry Little Christmas 36
 
< 0.1%
Rudolph The Red-Nosed Reindeer 35
 
< 0.1%
Mistletoe 33
 
< 0.1%
Other values (73598) 89349
99.6%

Length

2023-04-14T13:37:55.160952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15562
 
5.1%
the 7629
 
2.5%
you 3190
 
1.0%
a 2956
 
1.0%
of 2895
 
0.9%
me 2726
 
0.9%
i 2602
 
0.9%
vivo 2527
 
0.8%
in 2458
 
0.8%
ao 2267
 
0.7%
Other values (50550) 260771
85.3%

Most occurring characters

ValueCountFrequency (%)
215843
 
13.2%
e 138723
 
8.5%
a 108308
 
6.6%
o 97072
 
6.0%
i 87315
 
5.4%
n 75552
 
4.6%
r 73238
 
4.5%
t 64908
 
4.0%
s 53687
 
3.3%
l 50171
 
3.1%
Other values (2407) 664705
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1053612
64.7%
Uppercase Letter 267350
 
16.4%
Space Separator 215843
 
13.2%
Other Punctuation 24676
 
1.5%
Other Letter 19986
 
1.2%
Decimal Number 16527
 
1.0%
Dash Punctuation 14140
 
0.9%
Open Punctuation 7958
 
0.5%
Close Punctuation 7956
 
0.5%
Modifier Letter 483
 
< 0.1%
Other values (13) 991
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
395
 
2.0%
333
 
1.7%
320
 
1.6%
241
 
1.2%
234
 
1.2%
217
 
1.1%
209
 
1.0%
191
 
1.0%
187
 
0.9%
180
 
0.9%
Other values (2056) 17479
87.5%
Lowercase Letter
ValueCountFrequency (%)
e 138723
13.2%
a 108308
10.3%
o 97072
 
9.2%
i 87315
 
8.3%
n 75552
 
7.2%
r 73238
 
7.0%
t 64908
 
6.2%
s 53687
 
5.1%
l 50171
 
4.8%
u 38066
 
3.6%
Other values (135) 266572
25.3%
Uppercase Letter
ValueCountFrequency (%)
S 21704
 
8.1%
T 19971
 
7.5%
M 19231
 
7.2%
A 18875
 
7.1%
L 14643
 
5.5%
C 14156
 
5.3%
D 13742
 
5.1%
R 13407
 
5.0%
B 13291
 
5.0%
I 11872
 
4.4%
Other values (84) 106458
39.8%
Other Punctuation
ValueCountFrequency (%)
. 5969
24.2%
' 5256
21.3%
, 3796
15.4%
" 2884
11.7%
/ 1949
 
7.9%
: 1655
 
6.7%
& 1164
 
4.7%
! 931
 
3.8%
? 613
 
2.5%
122
 
0.5%
Other values (11) 337
 
1.4%
Nonspacing Mark
ValueCountFrequency (%)
́ 42
35.0%
25
20.8%
̃ 10
 
8.3%
̈ 9
 
7.5%
̧ 8
 
6.7%
̂ 6
 
5.0%
5
 
4.2%
̆ 5
 
4.2%
̊ 3
 
2.5%
̀ 2
 
1.7%
Other values (5) 5
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 3714
22.5%
0 3629
22.0%
1 3059
18.5%
9 1470
 
8.9%
3 929
 
5.6%
4 873
 
5.3%
5 851
 
5.1%
8 689
 
4.2%
7 678
 
4.1%
6 635
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 71
37.0%
~ 50
26.0%
| 22
 
11.5%
= 17
 
8.9%
> 15
 
7.8%
< 12
 
6.2%
2
 
1.0%
× 1
 
0.5%
1
 
0.5%
1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 7565
95.1%
[ 286
 
3.6%
70
 
0.9%
30
 
0.4%
2
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
{ 1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
10
30.3%
° 8
24.2%
8
24.2%
® 2
 
6.1%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 7564
95.1%
] 286
 
3.6%
70
 
0.9%
30
 
0.4%
2
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
} 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 14029
99.2%
76
 
0.5%
24
 
0.2%
9
 
0.1%
2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 37
74.0%
` 11
 
22.0%
^ 1
 
2.0%
˙ 1
 
2.0%
Final Punctuation
ValueCountFrequency (%)
328
86.5%
42
 
11.1%
» 9
 
2.4%
Initial Punctuation
ValueCountFrequency (%)
68
54.4%
48
38.4%
« 9
 
7.2%
Format
ValueCountFrequency (%)
8
80.0%
1
 
10.0%
 1
 
10.0%
Modifier Letter
ValueCountFrequency (%)
473
97.9%
10
 
2.1%
Currency Symbol
ValueCountFrequency (%)
$ 45
97.8%
1
 
2.2%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Number
ValueCountFrequency (%)
½ 1
50.0%
² 1
50.0%
Space Separator
ValueCountFrequency (%)
215843
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Control
ValueCountFrequency (%)
‚ 1
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1310229
80.4%
Common 288439
 
17.7%
Han 12429
 
0.8%
Cyrillic 10705
 
0.7%
Katakana 3882
 
0.2%
Hiragana 3592
 
0.2%
Inherited 119
 
< 0.1%
Hangul 58
 
< 0.1%
Greek 36
 
< 0.1%
Arabic 26
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
320
 
2.6%
217
 
1.7%
209
 
1.7%
191
 
1.5%
187
 
1.5%
180
 
1.4%
149
 
1.2%
125
 
1.0%
112
 
0.9%
104
 
0.8%
Other values (1836) 10635
85.6%
Latin
ValueCountFrequency (%)
e 138723
 
10.6%
a 108308
 
8.3%
o 97072
 
7.4%
i 87315
 
6.7%
n 75552
 
5.8%
r 73238
 
5.6%
t 64908
 
5.0%
s 53687
 
4.1%
l 50171
 
3.8%
u 38066
 
2.9%
Other values (144) 523189
39.9%
Common
ValueCountFrequency (%)
215843
74.8%
- 14029
 
4.9%
( 7565
 
2.6%
) 7564
 
2.6%
. 5969
 
2.1%
' 5256
 
1.8%
, 3796
 
1.3%
2 3714
 
1.3%
0 3629
 
1.3%
1 3059
 
1.1%
Other values (82) 18015
 
6.2%
Katakana
ValueCountFrequency (%)
333
 
8.6%
234
 
6.0%
175
 
4.5%
161
 
4.1%
156
 
4.0%
128
 
3.3%
109
 
2.8%
108
 
2.8%
102
 
2.6%
101
 
2.6%
Other values (69) 2275
58.6%
Hiragana
ValueCountFrequency (%)
395
 
11.0%
241
 
6.7%
177
 
4.9%
131
 
3.6%
114
 
3.2%
109
 
3.0%
106
 
3.0%
104
 
2.9%
101
 
2.8%
101
 
2.8%
Other values (63) 2013
56.0%
Cyrillic
ValueCountFrequency (%)
а 932
 
8.7%
о 891
 
8.3%
е 820
 
7.7%
и 639
 
6.0%
н 600
 
5.6%
р 536
 
5.0%
т 497
 
4.6%
л 453
 
4.2%
с 451
 
4.2%
к 384
 
3.6%
Other values (53) 4502
42.1%
Hangul
ValueCountFrequency (%)
3
 
5.2%
3
 
5.2%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
1
 
1.7%
1
 
1.7%
Other values (38) 38
65.5%
Greek
ValueCountFrequency (%)
ο 4
 
11.1%
τ 3
 
8.3%
ι 3
 
8.3%
Ψ 3
 
8.3%
φ 2
 
5.6%
ε 2
 
5.6%
Σ 1
 
2.8%
Ξ 1
 
2.8%
Χ 1
 
2.8%
σ 1
 
2.8%
Other values (15) 15
41.7%
Arabic
ValueCountFrequency (%)
د 4
15.4%
م 3
11.5%
ر 3
11.5%
ا 2
 
7.7%
ن 2
 
7.7%
ع 2
 
7.7%
ت 1
 
3.8%
گ 1
 
3.8%
چ 1
 
3.8%
خ 1
 
3.8%
Other values (6) 6
23.1%
Inherited
ValueCountFrequency (%)
́ 42
35.3%
25
21.0%
̃ 10
 
8.4%
̈ 9
 
7.6%
̧ 8
 
6.7%
̂ 6
 
5.0%
5
 
4.2%
̆ 5
 
4.2%
̊ 3
 
2.5%
̀ 2
 
1.7%
Other values (4) 4
 
3.4%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Unknown
ValueCountFrequency (%)
1
100.0%
Devanagari
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1585993
97.3%
CJK 12418
 
0.8%
None 11520
 
0.7%
Cyrillic 10705
 
0.7%
Katakana 4477
 
0.3%
Hiragana 3622
 
0.2%
Punctuation 560
 
< 0.1%
Diacriticals 87
 
< 0.1%
Hangul 58
 
< 0.1%
Arabic 26
 
< 0.1%
Other values (15) 56
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
215843
 
13.6%
e 138723
 
8.7%
a 108308
 
6.8%
o 97072
 
6.1%
i 87315
 
5.5%
n 75552
 
4.8%
r 73238
 
4.6%
t 64908
 
4.1%
s 53687
 
3.4%
l 50171
 
3.2%
Other values (84) 621176
39.2%
None
ValueCountFrequency (%)
é 1196
 
10.4%
ã 1106
 
9.6%
ó 853
 
7.4%
á 848
 
7.4%
í 700
 
6.1%
ç 664
 
5.8%
ä 593
 
5.1%
ü 482
 
4.2%
ê 479
 
4.2%
ı 475
 
4.1%
Other values (137) 4124
35.8%
Cyrillic
ValueCountFrequency (%)
а 932
 
8.7%
о 891
 
8.3%
е 820
 
7.7%
и 639
 
6.0%
н 600
 
5.6%
р 536
 
5.0%
т 497
 
4.6%
л 453
 
4.2%
с 451
 
4.2%
к 384
 
3.6%
Other values (53) 4502
42.1%
Katakana
ValueCountFrequency (%)
473
 
10.6%
333
 
7.4%
234
 
5.2%
175
 
3.9%
161
 
3.6%
156
 
3.5%
128
 
2.9%
122
 
2.7%
109
 
2.4%
108
 
2.4%
Other values (71) 2478
55.3%
Hiragana
ValueCountFrequency (%)
395
 
10.9%
241
 
6.7%
177
 
4.9%
131
 
3.6%
114
 
3.1%
109
 
3.0%
106
 
2.9%
104
 
2.9%
101
 
2.8%
101
 
2.8%
Other values (65) 2043
56.4%
Punctuation
ValueCountFrequency (%)
328
58.6%
68
 
12.1%
48
 
8.6%
42
 
7.5%
29
 
5.2%
24
 
4.3%
9
 
1.6%
8
 
1.4%
2
 
0.4%
1
 
0.2%
CJK
ValueCountFrequency (%)
320
 
2.6%
217
 
1.7%
209
 
1.7%
191
 
1.5%
187
 
1.5%
180
 
1.4%
149
 
1.2%
125
 
1.0%
112
 
0.9%
104
 
0.8%
Other values (1834) 10624
85.6%
Diacriticals
ValueCountFrequency (%)
́ 42
48.3%
̃ 10
 
11.5%
̈ 9
 
10.3%
̧ 8
 
9.2%
̂ 6
 
6.9%
̆ 5
 
5.7%
̊ 3
 
3.4%
̀ 2
 
2.3%
̌ 1
 
1.1%
̇ 1
 
1.1%
Misc Symbols
ValueCountFrequency (%)
10
52.6%
8
42.1%
1
 
5.3%
IPA Ext
ValueCountFrequency (%)
ə 10
100.0%
Arabic
ValueCountFrequency (%)
د 4
15.4%
م 3
11.5%
ر 3
11.5%
ا 2
 
7.7%
ن 2
 
7.7%
ع 2
 
7.7%
ت 1
 
3.8%
گ 1
 
3.8%
چ 1
 
3.8%
خ 1
 
3.8%
Other values (6) 6
23.1%
Hangul
ValueCountFrequency (%)
3
 
5.2%
3
 
5.2%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
1
 
1.7%
1
 
1.7%
Other values (38) 38
65.5%
Latin Ext Additional
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Arrows
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
2
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
50.0%
1
50.0%
PUA
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Devanagari
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Thai
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.198808
Minimum0
Maximum100
Zeros9447
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:55.257088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median33
Q349
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.58064
Coefficient of variation (CV)0.61992107
Kurtosis-0.77064595
Mean33.198808
Median Absolute Deviation (MAD)15
Skewness0.070863062
Sum2979261
Variance423.56276
MonotonicityNot monotonic
2023-04-14T13:37:55.344944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9447
 
10.5%
21 2271
 
2.5%
22 2258
 
2.5%
20 2058
 
2.3%
23 2038
 
2.3%
24 1726
 
1.9%
44 1696
 
1.9%
43 1626
 
1.8%
41 1609
 
1.8%
19 1597
 
1.8%
Other values (91) 63414
70.7%
ValueCountFrequency (%)
0 9447
10.5%
1 1085
 
1.2%
2 495
 
0.6%
3 301
 
0.3%
4 231
 
0.3%
5 506
 
0.6%
6 374
 
0.4%
7 419
 
0.5%
8 504
 
0.6%
9 503
 
0.6%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 2
 
< 0.1%
96 3
< 0.1%
95 2
 
< 0.1%
94 2
 
< 0.1%
93 5
< 0.1%
92 5
< 0.1%
91 5
< 0.1%

duration_ms
Real number (ℝ)

Distinct50696
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229144.37
Minimum8586
Maximum5237295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:55.430569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8586
5-th percentile112041.9
Q1173040
median213295.5
Q3264293
95-th percentile394001.4
Maximum5237295
Range5228709
Interquartile range (IQR)91253

Descriptive statistics

Standard deviation112945.78
Coefficient of variation (CV)0.49290228
Kurtosis331.97979
Mean229144.37
Median Absolute Deviation (MAD)44555.5
Skewness11.072801
Sum2.0563415 × 1010
Variance1.2756749 × 1010
MonotonicityNot monotonic
2023-04-14T13:37:55.520765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180000 83
 
0.1%
192000 77
 
0.1%
240000 72
 
0.1%
200000 47
 
0.1%
210000 46
 
0.1%
168000 46
 
0.1%
120000 43
 
< 0.1%
123453 43
 
< 0.1%
118840 41
 
< 0.1%
160000 41
 
< 0.1%
Other values (50686) 89201
99.4%
ValueCountFrequency (%)
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 1
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 1
< 0.1%
24000 1
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 1
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size788.7 KiB
False
82036 
True
 
7704
ValueCountFrequency (%)
False 82036
91.4%
True 7704
 
8.6%
2023-04-14T13:37:55.606420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct1174
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56216636
Minimum0
Maximum0.985
Zeros157
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:55.782919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23695
Q10.45
median0.576
Q30.692
95-th percentile0.825
Maximum0.985
Range0.985
Interquartile range (IQR)0.242

Descriptive statistics

Standard deviation0.17669202
Coefficient of variation (CV)0.31430558
Kurtosis-0.19354228
Mean0.56216636
Median Absolute Deviation (MAD)0.121
Skewness-0.39829181
Sum50448.809
Variance0.031220072
MonotonicityNot monotonic
2023-04-14T13:37:55.871473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.631 256
 
0.3%
0.647 252
 
0.3%
0.602 250
 
0.3%
0.582 248
 
0.3%
0.576 245
 
0.3%
0.598 244
 
0.3%
0.609 238
 
0.3%
0.524 238
 
0.3%
0.579 236
 
0.3%
0.607 234
 
0.3%
Other values (1164) 87299
97.3%
ValueCountFrequency (%)
0 157
0.2%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 3
< 0.1%

energy
Real number (ℝ)

Distinct2083
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63445847
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:55.961468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.142
Q10.457
median0.676
Q30.853
95-th percentile0.97
Maximum1
Range1
Interquartile range (IQR)0.396

Descriptive statistics

Standard deviation0.25660638
Coefficient of variation (CV)0.40444945
Kurtosis-0.60813378
Mean0.63445847
Median Absolute Deviation (MAD)0.193
Skewness-0.55999254
Sum56936.303
Variance0.065846834
MonotonicityNot monotonic
2023-04-14T13:37:56.050674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 202
 
0.2%
0.92 197
 
0.2%
0.988 195
 
0.2%
0.977 193
 
0.2%
0.931 192
 
0.2%
0.981 192
 
0.2%
0.979 189
 
0.2%
0.937 188
 
0.2%
0.938 187
 
0.2%
0.964 186
 
0.2%
Other values (2073) 87819
97.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 26
 
< 0.1%
0.999 88
0.1%
0.998 132
0.1%
0.997 129
0.1%
0.996 141
0.2%
0.995 186
0.2%
0.994 154
0.2%
0.993 163
0.2%
0.992 136
0.2%
0.991 161
0.2%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2835302
Minimum0
Maximum11
Zeros10352
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:56.129096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5599118
Coefficient of variation (CV)0.67377524
Kurtosis-1.2811037
Mean5.2835302
Median Absolute Deviation (MAD)3
Skewness-0.00014226818
Sum474144
Variance12.672972
MonotonicityNot monotonic
2023-04-14T13:37:56.194975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 10550
11.8%
0 10352
11.5%
2 9327
10.4%
9 8998
10.0%
1 8576
9.6%
5 7308
8.1%
4 7133
7.9%
11 7129
7.9%
6 6139
6.8%
10 5889
6.6%
Other values (2) 8339
9.3%
ValueCountFrequency (%)
0 10352
11.5%
1 8576
9.6%
2 9327
10.4%
3 2769
 
3.1%
4 7133
7.9%
5 7308
8.1%
6 6139
6.8%
7 10550
11.8%
8 5570
6.2%
9 8998
10.0%
ValueCountFrequency (%)
11 7129
7.9%
10 5889
6.6%
9 8998
10.0%
8 5570
6.2%
7 10550
11.8%
6 6139
6.8%
5 7308
8.1%
4 7133
7.9%
3 2769
 
3.1%
2 9327
10.4%

loudness
Real number (ℝ)

Distinct19480
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.4989938
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative89672
Negative (%)99.9%
Memory size1.4 MiB
2023-04-14T13:37:56.278208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-18.86205
Q1-10.32225
median-7.185
Q3-5.108
95-th percentile-3
Maximum4.532
Range54.063
Interquartile range (IQR)5.21425

Descriptive statistics

Standard deviation5.2215176
Coefficient of variation (CV)-0.61436892
Kurtosis5.4704059
Mean-8.4989938
Median Absolute Deviation (MAD)2.4265
Skewness-1.9598794
Sum-762699.7
Variance27.264246
MonotonicityNot monotonic
2023-04-14T13:37:56.366104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.956 58
 
0.1%
-5.662 53
 
0.1%
-9.336 49
 
0.1%
-7.57 42
 
< 0.1%
-7.282 40
 
< 0.1%
-4.177 39
 
< 0.1%
-6.264 38
 
< 0.1%
-4.457 38
 
< 0.1%
-8.169 34
 
< 0.1%
-4.499 33
 
< 0.1%
Other values (19470) 89316
99.5%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
1
57162 
0
32578 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

Length

2023-04-14T13:37:56.443059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T13:37:56.509649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

Most occurring characters

ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

Most occurring scripts

ValueCountFrequency (%)
Common 89740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 57162
63.7%
0 32578
36.3%

speechiness
Real number (ℝ)

Distinct1489
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087442333
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:56.579857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0283
Q10.036
median0.0489
Q30.0859
95-th percentile0.284
Maximum0.965
Range0.965
Interquartile range (IQR)0.0499

Descriptive statistics

Standard deviation0.11327768
Coefficient of variation (CV)1.2954558
Kurtosis26.567415
Mean0.087442333
Median Absolute Deviation (MAD)0.0165
Skewness4.5458345
Sum7847.075
Variance0.012831833
MonotonicityNot monotonic
2023-04-14T13:37:56.663743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0324 305
 
0.3%
0.0328 281
 
0.3%
0.0323 279
 
0.3%
0.0322 272
 
0.3%
0.033 268
 
0.3%
0.0321 267
 
0.3%
0.0341 267
 
0.3%
0.0326 264
 
0.3%
0.0363 263
 
0.3%
0.0367 263
 
0.3%
Other values (1479) 87011
97.0%
ValueCountFrequency (%)
0 157
0.2%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 2
 
< 0.1%
0.0225 1
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 1
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 5
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acousticness
Real number (ℝ)

Distinct5061
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32828497
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:56.756242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000124
Q10.0171
median0.188
Q30.625
95-th percentile0.956
Maximum0.996
Range0.996
Interquartile range (IQR)0.6079

Descriptive statistics

Standard deviation0.33832056
Coefficient of variation (CV)1.0305698
Kurtosis-1.0657855
Mean0.32828497
Median Absolute Deviation (MAD)0.18657
Skewness0.6557717
Sum29460.293
Variance0.1144608
MonotonicityNot monotonic
2023-04-14T13:37:56.839980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 257
 
0.3%
0.993 243
 
0.3%
0.994 234
 
0.3%
0.992 217
 
0.2%
0.991 197
 
0.2%
0.99 169
 
0.2%
0.989 155
 
0.2%
0.988 140
 
0.2%
0.987 139
 
0.2%
0.131 135
 
0.2%
Other values (5051) 87854
97.9%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-63
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 93
 
0.1%
0.995 257
0.3%
0.994 234
0.3%
0.993 243
0.3%
0.992 217
0.2%
0.991 197
0.2%
0.99 169
0.2%
0.989 155
0.2%
0.988 140
0.2%
0.987 139
0.2%

instrumentalness
Real number (ℝ)

Distinct5346
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17341537
Minimum0
Maximum1
Zeros29924
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:56.923758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.8 × 10-5
Q30.097625
95-th percentile0.911
Maximum1
Range1
Interquartile range (IQR)0.097625

Descriptive statistics

Standard deviation0.32384929
Coefficient of variation (CV)1.8674774
Kurtosis0.6795989
Mean0.17341537
Median Absolute Deviation (MAD)5.8 × 10-5
Skewness1.5639967
Sum15562.295
Variance0.10487836
MonotonicityNot monotonic
2023-04-14T13:37:57.008507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29924
33.3%
0.898 104
 
0.1%
0.911 102
 
0.1%
0.905 102
 
0.1%
0.9 101
 
0.1%
0.934 101
 
0.1%
0.913 100
 
0.1%
0.922 99
 
0.1%
0.921 98
 
0.1%
0.915 98
 
0.1%
Other values (5336) 58911
65.6%
ValueCountFrequency (%)
0 29924
33.3%
1 × 10-624
 
< 0.1%
1.01 × 10-638
 
< 0.1%
1.02 × 10-629
 
< 0.1%
1.03 × 10-628
 
< 0.1%
1.04 × 10-637
 
< 0.1%
1.05 × 10-633
 
< 0.1%
1.06 × 10-632
 
< 0.1%
1.07 × 10-634
 
< 0.1%
1.08 × 10-632
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

liveness
Real number (ℝ)

Distinct1722
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2169711
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:57.095420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0608
Q10.0982
median0.132
Q30.279
95-th percentile0.694
Maximum1
Range1
Interquartile range (IQR)0.1808

Descriptive statistics

Standard deviation0.19488489
Coefficient of variation (CV)0.89820664
Kurtosis4.0751488
Mean0.2169711
Median Absolute Deviation (MAD)0.0514
Skewness2.0620924
Sum19470.987
Variance0.037980119
MonotonicityNot monotonic
2023-04-14T13:37:57.183524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 1051
 
1.2%
0.108 1019
 
1.1%
0.109 975
 
1.1%
0.11 945
 
1.1%
0.107 896
 
1.0%
0.106 866
 
1.0%
0.112 835
 
0.9%
0.103 830
 
0.9%
0.105 827
 
0.9%
0.102 782
 
0.9%
Other values (1712) 80714
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 8
< 0.1%
0.991 3
 
< 0.1%
0.99 9
< 0.1%
0.989 13
< 0.1%
0.988 10
< 0.1%

valence
Real number (ℝ)

Distinct1790
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46947444
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:57.270602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.064095
Q10.249
median0.457
Q30.682
95-th percentile0.912
Maximum0.995
Range0.995
Interquartile range (IQR)0.433

Descriptive statistics

Standard deviation0.26286354
Coefficient of variation (CV)0.55991023
Kurtosis-1.0473933
Mean0.46947444
Median Absolute Deviation (MAD)0.216
Skewness0.12763729
Sum42130.636
Variance0.06909724
MonotonicityNot monotonic
2023-04-14T13:37:57.357173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 249
 
0.3%
0.962 186
 
0.2%
0 176
 
0.2%
0.963 171
 
0.2%
0.324 162
 
0.2%
0.964 156
 
0.2%
0.336 153
 
0.2%
0.305 152
 
0.2%
0.304 147
 
0.2%
0.549 143
 
0.2%
Other values (1780) 88045
98.1%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 2
< 0.1%
0.992 2
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45652
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.05813
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-14T13:37:57.545693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76.997
Q199.26275
median122.013
Q3140.077
95-th percentile174.994
Maximum243.372
Range243.372
Interquartile range (IQR)40.81425

Descriptive statistics

Standard deviation30.117651
Coefficient of variation (CV)0.24674841
Kurtosis-0.057443517
Mean122.05813
Median Absolute Deviation (MAD)21.629
Skewness0.18274444
Sum10953497
Variance907.07287
MonotonicityNot monotonic
2023-04-14T13:37:57.631434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.2%
71.078 42
 
< 0.1%
119.989 41
 
< 0.1%
130.594 41
 
< 0.1%
125.004 38
 
< 0.1%
151.925 38
 
< 0.1%
76.783 34
 
< 0.1%
130 33
 
< 0.1%
119.993 33
 
< 0.1%
161.948 32
 
< 0.1%
Other values (45642) 89251
99.5%
ValueCountFrequency (%)
0 157
0.2%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
4
79543 
3
 
7604
5
 
1585
1
 
846
0
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

Length

2023-04-14T13:37:57.704104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T13:37:57.775232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 89740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 79543
88.6%
3 7604
 
8.5%
5 1585
 
1.8%
1 846
 
0.9%
0 162
 
0.2%

track_genre
Categorical

Distinct113
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
acoustic
 
1000
alt-rock
 
999
tango
 
999
ambient
 
999
afrobeat
 
999
Other values (108)
84744 

Length

Max length17
Median length11
Mean length7.1468687
Min length3

Characters and Unicode

Total characters641360
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic

Common Values

ValueCountFrequency (%)
acoustic 1000
 
1.1%
alt-rock 999
 
1.1%
tango 999
 
1.1%
ambient 999
 
1.1%
afrobeat 999
 
1.1%
cantopop 999
 
1.1%
bluegrass 998
 
1.1%
forro 998
 
1.1%
study 998
 
1.1%
chicago-house 998
 
1.1%
Other values (103) 79753
88.9%

Length

2023-04-14T13:37:57.847974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acoustic 1000
 
1.1%
tango 999
 
1.1%
ambient 999
 
1.1%
afrobeat 999
 
1.1%
cantopop 999
 
1.1%
alt-rock 999
 
1.1%
bluegrass 998
 
1.1%
forro 998
 
1.1%
study 998
 
1.1%
chicago-house 998
 
1.1%
Other values (103) 79753
88.9%

Most occurring characters

ValueCountFrequency (%)
a 56636
 
8.8%
e 55400
 
8.6%
o 52430
 
8.2%
r 42776
 
6.7%
n 38375
 
6.0%
i 37341
 
5.8%
s 35237
 
5.5%
t 33400
 
5.2%
c 32505
 
5.1%
l 32482
 
5.1%
Other values (15) 224778
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 613270
95.6%
Dash Punctuation 28090
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 56636
 
9.2%
e 55400
 
9.0%
o 52430
 
8.5%
r 42776
 
7.0%
n 38375
 
6.3%
i 37341
 
6.1%
s 35237
 
5.7%
t 33400
 
5.4%
c 32505
 
5.3%
l 32482
 
5.3%
Other values (14) 196688
32.1%
Dash Punctuation
ValueCountFrequency (%)
- 28090
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 613270
95.6%
Common 28090
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 56636
 
9.2%
e 55400
 
9.0%
o 52430
 
8.5%
r 42776
 
7.0%
n 38375
 
6.3%
i 37341
 
6.1%
s 35237
 
5.7%
t 33400
 
5.4%
c 32505
 
5.3%
l 32482
 
5.3%
Other values (14) 196688
32.1%
Common
ValueCountFrequency (%)
- 28090
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 56636
 
8.8%
e 55400
 
8.6%
o 52430
 
8.2%
r 42776
 
6.7%
n 38375
 
6.0%
i 37341
 
5.8%
s 35237
 
5.5%
t 33400
 
5.2%
c 32505
 
5.1%
l 32482
 
5.1%
Other values (15) 224778
35.0%

Interactions

2023-04-14T13:37:52.993133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:40.845339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.017835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.998013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.960312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.937559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.993915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.953835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.924456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.892839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.965964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.951804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.931266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.066496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:40.950496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.094707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.074930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.035094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.017570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.064200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.029784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.001318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.967766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.043967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.026738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.004033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.138212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.042102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.172280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.146731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.107846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.094532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.142892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.100446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.072980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.041112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.122192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.101124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.078610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.212075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.146541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.246698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.222261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.181580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.171463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.223425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.176191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.150907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.117998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.195583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.181472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.153241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.286602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.224711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.323548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.296030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.259744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.247039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.296867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.249905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.227016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.284697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.270925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.261677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.229862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.363528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.310370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.402441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.372666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.331832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.321613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.369558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.325231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.299080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.359897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.341401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.336669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.302533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.436483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.386770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.476161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.443479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.405762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.394286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.440705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.404470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.374881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.431640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.417080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.408573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.378488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.508456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.464875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.550828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.516777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.479435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.468807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.511601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.478566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.448344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.506163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.491151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.482590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.453258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.583564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.549530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.629318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.590264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.553968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.626448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.585761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.550875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.523387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.577568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.564994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.556063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.525164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.657062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.634822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.702012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.666908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.626450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.699078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.658317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.627199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.596538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.651781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.638781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.634256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.600676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.732254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.715691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.779383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.738803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.702303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.770324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.732562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.701781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.671623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.730347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.714698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.707438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.676759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.806374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.794735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.849474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.812517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.779859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.846034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.807409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.777169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.744760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.810260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.786551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.780966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.748481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:53.881443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:41.942562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:42.924136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:43.886046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:44.859977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:45.918071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:46.882436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:47.850687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:48.819264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:49.888711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:50.881084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:51.854187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T13:37:52.821564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-14T13:37:57.926645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0popularityduration_msdanceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoexplicitmodetime_signature
Unnamed: 01.0000.039-0.027-0.021-0.057-0.009-0.039-0.0360.098-0.0730.0410.033-0.0240.1210.0700.077
popularity0.0391.0000.0130.055-0.0160.0040.067-0.0670.010-0.124-0.011-0.0110.0080.0930.0350.046
duration_ms-0.0270.0131.000-0.0830.1150.0170.033-0.124-0.1790.118-0.041-0.1700.0530.0110.0070.036
danceability-0.0210.055-0.0831.0000.0500.0330.1160.152-0.054-0.158-0.1480.477-0.0480.1480.0770.284
energy-0.057-0.0160.1150.0501.0000.0430.7530.351-0.712-0.0430.1820.2050.2500.1220.0830.162
key-0.0090.0040.0170.0330.0431.0000.0290.040-0.0410.003-0.0060.0250.0090.0440.2510.022
loudness-0.0390.0670.0330.1160.7530.0291.0000.218-0.537-0.2990.1150.2270.2090.1080.0380.155
speechiness-0.036-0.067-0.1240.1520.3510.0400.2181.000-0.216-0.0510.0950.0820.1090.3290.0640.087
acousticness0.0980.010-0.179-0.054-0.712-0.041-0.537-0.2161.000-0.094-0.045-0.023-0.2270.1010.0960.142
instrumentalness-0.073-0.1240.118-0.158-0.0430.003-0.299-0.051-0.0941.000-0.105-0.334-0.0130.1120.0630.068
liveness0.041-0.011-0.041-0.1480.182-0.0060.1150.095-0.045-0.1051.0000.0080.0150.0480.0250.042
valence0.033-0.011-0.1700.4770.2050.0250.2270.082-0.023-0.3340.0081.0000.0750.0710.0360.115
tempo-0.0240.0080.053-0.0480.2500.0090.2090.109-0.227-0.0130.0150.0751.0000.0370.0220.497
explicit0.1210.0930.0110.1480.1220.0440.1080.3290.1010.1120.0480.0710.0371.0000.0340.060
mode0.0700.0350.0070.0770.0830.2510.0380.0640.0960.0630.0250.0360.0220.0341.0000.028
time_signature0.0770.0460.0360.2840.1620.0220.1550.0870.1420.0680.0420.1150.4970.0600.0281.000

Missing values

2023-04-14T13:37:54.071084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-14T13:37:54.362491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
005SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acoustic
114qPNDBW1i3p13qLCt0Ki3ABen WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acoustic
221iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acoustic
336lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acoustic
445vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acoustic
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